State estimation is an essential component of autonomous systems, usually relying on sensor fusion that integrates data from cameras, LiDARs and IMUs. Recently, radars have shown the potential to improve the accuracy and robustness of state estimation and perception, especially in challenging environmental conditions such as adverse weather and low-light scenarios. In this paper, we present a framework for ego-velocity estimation, which we call RAVE, that relies on 3D automotive radar data and encompasses zero velocity detection, outlier rejection, and velocity estimation. In addition, we propose a simple filtering method to discard infeasible ego-velocity estimates. We also conduct a systematic analysis of how different existing outlier rejection techniques and optimization loss functions impact estimation accuracy. Our evaluation on three open-source datasets demonstrates the effectiveness of the proposed filter and a significant positive impact of RAVE on the odometry accuracy. Furthermore, we release an open-source implementation of the proposed framework for radar ego-velocity estimation accompanied with a ROS interface.

翻译：状态估计是自主系统的关键组成部分，通常依赖于融合相机、激光雷达和惯性测量单元数据的传感器融合技术。近年来，雷达展现出提升状态估计与感知精度及鲁棒性的潜力，尤其在恶劣天气、低光照等挑战性环境条件下。本文提出一种自车速度估计框架（称为RAVE），该框架基于三维汽车雷达数据，包含零速检测、异常值剔除和速度估计模块。此外，我们提出一种简单的滤波方法以剔除不可行的自车速度估计值。我们系统分析了现有不同异常值剔除技术与优化损失函数对估计精度的影响。在三个开源数据集上的评估结果表明，所提滤波方法具有有效性，且RAVE对里程计精度产生显著正向影响。同时，我们开源了所提雷达自车速度估计框架的实现代码，并提供了ROS接口。